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Contraband Detection of Millimeter Wave Image for Postal Security Check Using Spatial Transformer-Feature Fusion Network

Applied Optics(2024)SCI 3区

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Abstract
The image resolution and contraband object detection accuracy are the two key factors for security checks based on millimeter wave imaging techniques. In this paper, a homemade real-time millimeter imaging system for small package security inspection is used to obtain about 400 raw images of envelopes containing multi -contraband objects like guns and knives. After pre -processing, spatial transformer-feature fusion (ST-FF) adapted single -shot multi -box detector (SSD) networks are used to detect the contraband objects of postal packages. The experiments reveal that the spatial-transformed-feature fusion deep learning networks demonstrate better mean average precision (mAP) performance than traditional single networks in detecting contraband objects of different scales, orientations, and distortions, and prove the great potential for security checks based on millimeter wave imaging. (c) 2024 Optica Publishing Group
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要点】:本文提出了一种基于空间转换特征融合网络的毫米波图像违禁品检测方法,提高了违禁品检测的准确性和效率。

方法】:采用自制的实时毫米波成像系统获取包含多种违禁品的邮件图像,通过空间转换特征融合(ST-FF)适应的单次多框检测器(SSD)网络进行违禁品检测。

实验】:使用400张经过预处理的邮件图像作为数据集,实验结果表明,空间转换特征融合深度学习网络在检测不同尺度、方向和扭曲的违禁品对象时,具有比传统单一网络更好的平均精度(mAP)表现。